Reference : Recurrent neural network prediction of steam production in a Kraft recovery boiler
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Chemical engineering
http://hdl.handle.net/2268/94250
Recurrent neural network prediction of steam production in a Kraft recovery boiler
English
Sainlez, Matthieu mailto [Université de Liège - ULg > > > Form.doct. sc. ingé. (chim. appl. - Bologne)]
Heyen, Georges [Université de Liège - ULg > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques) >]
2011
First edition 2011
21st European Symposium on Computer Aided Process Engineering (Part B)
Pistikopoulos, E. N.
Georgiadis, M. C.
Kokossis, A. C.
Elsevier
Computer-Aided Chemical Engineering, 29
1784-1788
Yes
No
International
978-0-444-54298-4
Amsterdam
The Netherlands
ESCAPE21
May 29 - June 1, 2011
EFCE - European Federation of Chemical Engineering
Chalkidiki
Greece
[en] recurrent neural networks ; Kraft recovery boiler ; steam production
[en] In this paper, neural networks approaches are compared for predicting the high pressure
(HP) steam flow rate from a Kraft recovery boiler. We apply two types of neural networks:
a static multilayer perceptron and a dynamic Elman’s recurrent neural network. Starting
from a one-day database of raw process data related to the boiler, the goal is to model
and predict the next 12-hours of HP steam flow production from the boiler to the steam
turbine. The results illustrate the potential of the dynamic approach in this task.
http://hdl.handle.net/2268/94250

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Restricted access
Sainlez_Review.pdfAuthor postprint1.39 MBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.